{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a10e8358",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "239fc8ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入.csv文件，用read_csv(),并读取前100行以及各列\n",
    "df = pd.read_csv('Factors1.csv',encoding='gbk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a71c1ccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province</th>\n",
       "      <th>index</th>\n",
       "      <th>gdp</th>\n",
       "      <th>resident</th>\n",
       "      <th>house</th>\n",
       "      <th>income</th>\n",
       "      <th>inflow</th>\n",
       "      <th>household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>深圳市</td>\n",
       "      <td>0.914</td>\n",
       "      <td>30664.85</td>\n",
       "      <td>1768</td>\n",
       "      <td>71209</td>\n",
       "      <td>70847</td>\n",
       "      <td>1178.80</td>\n",
       "      <td>589.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海市</td>\n",
       "      <td>0.791</td>\n",
       "      <td>43214.85</td>\n",
       "      <td>2489</td>\n",
       "      <td>66801</td>\n",
       "      <td>78027</td>\n",
       "      <td>1009.00</td>\n",
       "      <td>1480.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>北京市</td>\n",
       "      <td>0.758</td>\n",
       "      <td>40269.60</td>\n",
       "      <td>2189</td>\n",
       "      <td>63809</td>\n",
       "      <td>75002</td>\n",
       "      <td>788.20</td>\n",
       "      <td>1400.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>广州市</td>\n",
       "      <td>0.723</td>\n",
       "      <td>28231.97</td>\n",
       "      <td>1881</td>\n",
       "      <td>46145</td>\n",
       "      <td>68908</td>\n",
       "      <td>888.92</td>\n",
       "      <td>992.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>武汉市</td>\n",
       "      <td>0.646</td>\n",
       "      <td>17716.76</td>\n",
       "      <td>1364</td>\n",
       "      <td>19846</td>\n",
       "      <td>41253</td>\n",
       "      <td>316.50</td>\n",
       "      <td>1047.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>赣州市</td>\n",
       "      <td>0.248</td>\n",
       "      <td>4169.37</td>\n",
       "      <td>879</td>\n",
       "      <td>8195</td>\n",
       "      <td>26899</td>\n",
       "      <td>9.80</td>\n",
       "      <td>869.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>宜春市</td>\n",
       "      <td>0.248</td>\n",
       "      <td>3191.28</td>\n",
       "      <td>500</td>\n",
       "      <td>7754</td>\n",
       "      <td>28268</td>\n",
       "      <td>-32.10</td>\n",
       "      <td>532.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>驻马店市</td>\n",
       "      <td>0.248</td>\n",
       "      <td>3100.00</td>\n",
       "      <td>700</td>\n",
       "      <td>5467</td>\n",
       "      <td>22440</td>\n",
       "      <td>9.20</td>\n",
       "      <td>690.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>曲靖市</td>\n",
       "      <td>0.225</td>\n",
       "      <td>3393.91</td>\n",
       "      <td>576</td>\n",
       "      <td>5877</td>\n",
       "      <td>30545</td>\n",
       "      <td>2.68</td>\n",
       "      <td>573.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>鄂尔多斯</td>\n",
       "      <td>0.196</td>\n",
       "      <td>4715.70</td>\n",
       "      <td>215</td>\n",
       "      <td>8253</td>\n",
       "      <td>45638</td>\n",
       "      <td>16.36</td>\n",
       "      <td>198.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   province  index       gdp  resident  house  income   inflow  household\n",
       "0      深圳市   0.914  30664.85      1768  71209   70847  1178.80     589.20\n",
       "1     上海市    0.791  43214.85      2489  66801   78027  1009.00    1480.00\n",
       "2     北京市    0.758  40269.60      2189  63809   75002   788.20    1400.40\n",
       "3      广州市   0.723  28231.97      1881  46145   68908   888.92     992.08\n",
       "4      武汉市   0.646  17716.76      1364  19846   41253   316.50    1047.50\n",
       "..      ...    ...       ...       ...    ...     ...      ...        ...\n",
       "95     赣州市   0.248   4169.37       879   8195   26899     9.80     869.20\n",
       "96     宜春市   0.248   3191.28       500   7754   28268   -32.10     532.10\n",
       "97    驻马店市   0.248   3100.00       700   5467   22440     9.20     690.80\n",
       "98     曲靖市   0.225   3393.91       576   5877   30545     2.68     573.32\n",
       "99    鄂尔多斯   0.196   4715.70       215   8253   45638    16.36     198.64\n",
       "\n",
       "[100 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab24e7bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入.csv文件，用read_csv(),并读取前100行以及0,1,4三列\n",
    "df = pd.read_csv('Factors1.csv',encoding='gbk',nrows=101,usecols=[0,1,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b91cda04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province</th>\n",
       "      <th>index</th>\n",
       "      <th>house</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>深圳市</td>\n",
       "      <td>0.914</td>\n",
       "      <td>71209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海市</td>\n",
       "      <td>0.791</td>\n",
       "      <td>66801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>北京市</td>\n",
       "      <td>0.758</td>\n",
       "      <td>63809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>广州市</td>\n",
       "      <td>0.723</td>\n",
       "      <td>46145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>武汉市</td>\n",
       "      <td>0.646</td>\n",
       "      <td>19846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>赣州市</td>\n",
       "      <td>0.248</td>\n",
       "      <td>8195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>宜春市</td>\n",
       "      <td>0.248</td>\n",
       "      <td>7754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>驻马店市</td>\n",
       "      <td>0.248</td>\n",
       "      <td>5467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>曲靖市</td>\n",
       "      <td>0.225</td>\n",
       "      <td>5877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>鄂尔多斯</td>\n",
       "      <td>0.196</td>\n",
       "      <td>8253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   province  index  house\n",
       "0      深圳市   0.914  71209\n",
       "1     上海市    0.791  66801\n",
       "2     北京市    0.758  63809\n",
       "3      广州市   0.723  46145\n",
       "4      武汉市   0.646  19846\n",
       "..      ...    ...    ...\n",
       "95     赣州市   0.248   8195\n",
       "96     宜春市   0.248   7754\n",
       "97    驻马店市   0.248   5467\n",
       "98     曲靖市   0.225   5877\n",
       "99    鄂尔多斯   0.196   8253\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df660010",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   province  100 non-null    object \n",
      " 1   index     100 non-null    float64\n",
      " 2   house     100 non-null    int64  \n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 2.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#了解数据概况，100行，3列\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b22e8044",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = df[\"index\"].to_list()\n",
    "house = df[\"house\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b0c87731",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyecharts.options as opts\n",
    "from pyecharts.charts import Scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8ed0e760",
   "metadata": {},
   "outputs": [],
   "source": [
    "scatter = (\n",
    "    Scatter()\n",
    "    .add_xaxis(\n",
    "        xaxis_data=house#定义x数据\n",
    "    )\n",
    "    .add_yaxis(\n",
    "        series_name=\"\",\n",
    "        y_axis=index,#定义y数据\n",
    "        symbol_size=4,#点的大小\n",
    "        label_opts=opts.LabelOpts(is_show=False)#不显示数值\n",
    "    )\n",
    "    .set_global_opts(#全局参数\n",
    "        xaxis_opts=opts.AxisOpts(type_=\"value\"),\n",
    "        yaxis_opts=opts.AxisOpts(type_=\"value\"),#y数据作为数值处理\n",
    "        title_opts=opts.TitleOpts(title=\"Diagram of (index-house)\",pos_left=\"ceter\")\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "049f3ef5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"4f9330727ac6425e97fd75f5c998e0c0\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_4f9330727ac6425e97fd75f5c998e0c0 = echarts.init(\n",
       "                    document.getElementById('4f9330727ac6425e97fd75f5c998e0c0'), 'white', {renderer: 'canvas'});\n",
       "                var option_4f9330727ac6425e97fd75f5c998e0c0 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"scatter\",\n",
       "            \"symbolSize\": 4,\n",
       "            \"data\": [\n",
       "                [\n",
       "                    71209,\n",
       "                    0.914\n",
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       "                [\n",
       "                    66801,\n",
       "                    0.791\n",
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       "                [\n",
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       "                [\n",
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       "                [\n",
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       "                [\n",
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       "                [\n",
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       "                    0.465\n",
       "                ],\n",
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       "                    0.461\n",
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       "                    0.431\n",
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       "                    0.421\n",
       "                ],\n",
       "                [\n",
       "                    15645,\n",
       "                    0.417\n",
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       "                [\n",
       "                    12731,\n",
       "                    0.417\n",
       "                ],\n",
       "                [\n",
       "                    14292,\n",
       "                    0.414\n",
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       "                [\n",
       "                    9458,\n",
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       "                [\n",
       "                    21893,\n",
       "                    0.406\n",
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